Vaccine Prioritization Model - At the Individual Level
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● Everyone in the province that we have the health data for will
be clustered and assigned a “provincial priority level” (PPL)
based on the cluster they belong to
● Clusters are assigned a “priority level” (PPL) ranging from 1 to 10
○ 1 Highest = Highest Priority | 10 = Lowest priority
○ Priority will be assigned based on the cluster's averages
of following factors (In order):
■ Age (old = higher priority)
■ Underlying health conditions
■ Income (low = higher priority)
● We will be using unsupervised machine learning models such
as K-means to cluster into k (we chose 10) clusters based on
the following numerical features:
● We will try to predict a PPL for users who have not been
assigned a PPL at the provincial level
● These users will have to fill in a questionnaire when they
register to receive their vaccine with information such as:
○ Age
○ Obesity (1,0)
○ Hypertension (1,0)
○ Diabetes (1,0)
○ Other health conditions
○ Postal Code
○ Family Size
○ Essential Work
● Then they will be classified into the same 10 PPLs
● We will be using the following classification algorithms:
● Age
● Weight
● Income
● Essential
Worker
● MAX V02
● Blood Sugar
Level
● Cholesterol Level
● Blood pressure
● Postal Code Density
● Family Size
● Workplace Size (0
for WFH)
PPL 4
PPL
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Note: Score is a measure of accuracy of the classifier on PPLs prediction on a
randomly generated mock dataset
Supervised Learning (For people without data)
Unsupervised Learning (For people with data)
Demographics Health Conditions Exposure & Spread
Classifier Score
KNeighborsClassifier 44%
SVC 40%
DecisionTreeClassifier 28%
RandomForestClassifier 32%
AdaBoostClassifier 40%
Classifier Score
GaussianNB 38%
LinearDiscriminantAnalysis 42%
QuadraticDiscriminantAnalysis 30%
NeuralNetwork
N/A
GradientBoostingClassifier 40%